关键词: 高维特征匹配, 最近邻搜索, 图像检索, 子向量距离索引

Abstract: Local invariant features have been widely applied in many computer vision applications and high-dimensional image feature matching is a core part of solving these problems. In this paper, it proposes a new indexing structure for the high-dimensional feature matching, which is based on the distance of the sub-vectors. The algorithm converts the feature vectors into one dimensional indexing value and only searches the features indexed by the same value in nearest neighbor searching process so that the searching speed can be greatly improved and high searching accuracy can be reached at the same time. Experimental results demonstrate the efficiency and effectiveness of the proposed methods in extensive image matching and image retrieval applications.

Key words: high dimensional feature matching, nearest neighbor searching, image retrieve, indexing sub-vector distance